CWM — Consumer World Model · Pemahaman konsumen · Inti
Pahami perilaku, tinjau sinyal dan temukan audiens serupa melalui ruang kerja, API atau MCP.
500.000 kredit sambutan untuk akun CWM baru
One member prediction returns next behaviors and personal purchase windows, combining consumer signals with brand demand context when the tenant fusion head is ready.
Interactive example
All states and numbers are synthetic examples, not live predictions or workspace readiness. No customer data is sent.
Behavior predictions remain available. Purchase windows stay unavailable until the brand's fusion head is trained and validated.
One final distribution over this example's four supported events. Next-event probabilities are uncalibrated.
| Next event | Probability |
|---|---|
| Purchase | 37% |
| Portal visit | 28% |
| Email click | 21% |
| Coupon redemption | 14% |
Email sent is an input context event, not a prediction target. Its probability is null. Other unsupported or uncovered events also stay unavailable.
Personal CWM signals + TimesFM brand demand context.
Chance of at least one purchase. These windows are cumulative; do not add them together or to the event distribution.
One member request returns distribution and purchaseWindows together.
curl --request GET \
"https://YOUR_API_HOST/api/v2/cwm/members/YOUR_MEMBER_UUID/next-behaviors" \
--header "Authorization: Bearer YOUR_API_KEY"{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "cwm_predict_next_behaviors",
"arguments": {
"member_id": "YOUR_MEMBER_UUID"
}
}
}Replace the API host, member UUID and API key with your workspace values.
Required access · predictions:read returns behaviors. Also grant metrics:read (or *) to enable purchase windows when the model is ready.
Synthetic excerpt only. Brand-model responses also include the full event catalog and coverage metadata; public-prior references use a separate six-class vocabulary.
{
"predictionKind": "next_event",
"unit": "probability_0_1",
"status": "degraded",
"distribution": {
"status": "ok",
"vocabulary": "member_event",
"normalization": "conditional_on_supported_events",
"calibrated": false,
"rows": [
{
"key": "purchase.completed",
"label": "Purchase",
"probability": 0.37,
"rank": 1,
"availability": "predicted"
},
{
"key": "portal.visited",
"label": "Portal visit",
"probability": 0.28,
"rank": 2,
"availability": "predicted"
},
{
"key": "email.clicked",
"label": "Email clicked",
"probability": 0.21,
"rank": 3,
"availability": "predicted"
},
{
"key": "coupon.redeemed",
"label": "Coupon redeemed",
"probability": 0.14,
"rank": 4,
"availability": "predicted"
},
{
"key": "email.sent",
"label": "Email sent",
"probability": null,
"rank": null,
"availability": "context_only"
}
]
},
"purchaseWindows": {
"unit": "probability_0_1",
"probabilities": {
"7": null,
"30": null,
"90": null
},
"calibrated": false,
"envelope": {
"status": "unavailable",
"backend": "cwm-timesfm-2.5",
"reasonCodes": [
"fusion_not_trained"
]
}
},
"reasonCodes": [
"fusion_not_trained"
]
}Synthetic data · TimesFM 2.5
An example forecast from 128 days of synthetic history. Compare the recent pattern with future daily demand and its quantiles.
Synthetic demand units
| Date (UTC) | Point forecast | q10–q90 |
|---|---|---|
| 2026-10-05 | 22.9 | 22.8–23.0 |
| 2026-10-06 | 21.3 | 21.2–21.4 |
| 2026-10-07 | 18.7 | 18.6–18.8 |
| 2026-10-08 | 17.1 | 17.0–17.1 |
| 2026-10-09 | 17.7 | 17.6–17.7 |
| 2026-10-10 | 20.0 | 20.0–20.1 |
| 2026-10-11 | 22.3 | 22.3–22.4 |
| 2026-10-12 | 22.9 | 22.9–22.9 |
| 2026-10-13 | 21.3 | 21.2–21.3 |
| 2026-10-14 | 18.7 | 18.6–18.7 |
| 2026-10-15 | 17.1 | 17.0–17.2 |
| 2026-10-16 | 17.7 | 17.6–17.7 |
| 2026-10-17 | 20.0 | 19.9–20.1 |
| 2026-10-18 | 22.4 | 22.3–22.4 |
| 2026-10-19 | 22.9 | 22.8–23.0 |
| 2026-10-20 | 21.3 | 21.2–21.4 |
| 2026-10-21 | 18.7 | 18.6–18.7 |
| 2026-10-22 | 17.1 | 17.0–17.1 |
| 2026-10-23 | 17.7 | 17.6–17.7 |
| 2026-10-24 | 20.0 | 19.9–20.0 |
| 2026-10-25 | 22.3 | 22.3–22.4 |
| 2026-10-26 | 22.9 | 22.8–22.9 |
| 2026-10-27 | 21.3 | 21.2–21.3 |
| 2026-10-28 | 18.7 | 18.6–18.7 |
| 2026-10-29 | 17.1 | 17.0–17.2 |
| 2026-10-30 | 17.7 | 17.6–17.7 |
| 2026-10-31 | 20.0 | 20.0–20.1 |
| 2026-11-01 | 22.4 | 22.3–22.4 |
| 2026-11-02 | 22.9 | 22.8–22.9 |
| 2026-11-03 | 21.3 | 21.2–21.3 |
Synthetic example, generated with TimesFM 2.5 weights. The forecast starts on 2026-10-05 (UTC); dates are fixed for the demo. Quantile coverage is uncalibrated. Values do not represent a customer's demand or individual purchase probabilities.
Receive one final probability distribution and ranking. The evaluated brand model takes priority, with up to 23 covered targets. When it is unavailable, internal public-prior models form a single six-class reference. Unsupported and context-only events remain unavailable.
Included automatically in the same member response: a tenant-private lightweight head combines CWM signals with TimesFM brand trends. Ready heads serve directly; missing training or evaluation leaves only the purchase windows unavailable.
Where the TimesFM 2.5 service is configured and daily history is complete, forecast revenue, orders or units sold for up to 90 days, with point estimates and nine quantiles.
Next-event probabilities sum to 100% over supported candidates and are uncalibrated. Purchase windows separately estimate at least one purchase within 7/30/90 days and are cumulative, never normalized with event classes. Elapsed silence is not conditioned in the event vector. The public-prior reference has not earned workspace validation.
Demand quantiles describe aggregate uncertainty; they are not individual purchase intervals and have not been calibrated. Missing data remain unavailable. No accuracy lift is asserted before your own evaluation.
Tinjau perilaku yang diamati, keadaan konsumen dan sinyal model standar untuk pembelian, ketidakaktifan lama serta penghapusan barang dari keranjang. Data yang hilang dan batas setiap sinyal ditampilkan.
Jelajahi kemiripan perilaku, kelompok dan anomali. Kemiripan membantu meneliti audiens; bukan jaminan tingkat konversi.
Gunakan CWM melalui konsol, REST API atau MCP dalam aplikasi dan klien AI. Kunci dengan izin terbatas mengontrol akses ke ruang kerja.
Analisis memerlukan format data yang didukung dan riwayat perilaku yang cukup. Sinyal yang tidak tersedia ditandai dengan jelas. Akun baru tidak berisi data pelanggan atau prediksi.
Tinjau analisis di konsol atau izinkan aplikasi dan klien AI mengakses CWM. Impor data yang didukung, buat kunci dengan izin terbatas dan lihat operasi yang tersedia dalam referensi publik.
Buka referensi API dan MCP →Akun baru menerima kredit. Operasi yang memenuhi syarat menggunakannya sesuai tarif yang dipublikasikan. Beli kredit tambahan dan lihat saldo serta penggunaan di ruang kerja Anda.
Daftar dengan email dan kata sandi atau Google, lalu setujui perjanjian pengguna CWM. Ketersediaan layanan menurut wilayah ditampilkan saat pendaftaran.
Coba CWM →Pemodelan perilaku, kembaran digital, validasi model per merek dan simulasi dijelaskan terpisah dari operasi standar yang saat ini tersedia di ruang kerja mandiri.
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Tangkap perilaku, putuskan tindakan, aktifkan kanal dan bangun loyalitas. Gunakan hasil untuk siklus berikutnya.
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CWM
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500.000 kredit sambutan untuk akun CWM baru
Harness + Loop Marketing
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